serene/docs/videos.org

#+TITLE: How to build a compiler with LLVM and MLIR
#+SEQ_TODO: TODO(t/!) NEXT(n/!) BLOCKED(b@/!) | DONE(d%) CANCELLED(c@/!) FAILED(f@/!)
#+TAGS: READER(r) MISC(m)
#+STARTUP: logdrawer logdone logreschedule indent content align constSI entitiespretty overview

* DONE Episode 1 - Introduction
** What is it all about?
  - Create a programming lang
  - Guide for contributors
  - A LLVM/MLIR guide
** The Plan
  - Git branches
  - No live coding
  - Feel free to contribute
** Serene and a bit of history
  - Other Implementations
  - Requirements
    - C++ 14
    - CMake
  - Repository: https://devheroes.codes/Serene
  - Website: lxsameer.com
    Email: lxsameer@gnu.org
* DONE Episode 2 - Basic Setup
CLOSED: [2021-07-10 Sat 09:04]
** Installing Requirements
*** LLVM and Clang
- mlir-tblgen
*** ccache (optional)
** Building Serene and the =builder=
- git hooks
** Source tree structure
** =dev.org= resources and TODOs
* DONE Episode 3 - Overview
CLOSED: [2021-07-19 Mon 09:41]
** Generic Compiler
- [[https://www.cs.princeton.edu/~appel/modern/ml/whichver.html][Modern Compiler Implementation in ML: Basic Techniques]]
- [[https://suif.stanford.edu/dragonbook/][Compilers: Principles, Techniques, and Tools (The Dragon Book)]]
*** Common Steps
- Frontend
  - Lexical analyzer (Lexer)
  - Syntax analyzer (Parser)
  - Semantic analyzer
- Middleend
  - Intermediate code generation
  - Code optimizer
- Backend
  - Target code generation
** LLVM
[[llvm.org]]
*** Watch [[https://www.youtube.com/watch?v=J5xExRGaIIY][Introdution to LLVM]]
*** Quick overview
Deducted from https://www.aosabook.org/en/llvm.html
[[./imgs/llvm_dia.svg]]
- It's a set of libraries to create a compiler.
- Well engineered.
- we can focus only on the fronted of the compiler and what is
  actually important to us and leave the tricky stuff to LLVM.
- LLVM IR enables us to use multiple languages together.
- It supports many targets.
- We can benefit from already made IR level optimizers.
- ....

** MLIR
[[mlir.llvm.org]]
[[./imgs/mlir_dia.svg]]

- With MLIR dialects provide higher level semantics than LLVM IR.
- It's easier to reason about higher level IR that is modeled after
  the AST rather than a low level IR.
- We can use the pass infrastructure to efficiently process and transform the IR.
- With many ready to use dialects we can really focus on our language and us the other
  dialect when ever necessary.
- ...
** Serene
*** A Compiler frontend
*** Flow
- =serenec= in parses the command lines args
- =reader= reads the input file and generates an =AST=
- =semantic analyzer= walks the =AST= and generates a new =AST= and rewrites
  the necessary nodes.
- =slir= generator generates =slir= dialect code from =AST=.
- We lower =slir= to other dialects of the *MLIR* which we call the result =mlir=.
- Then, We lower everything to the =LLVMIR dialect= and call it =lir= (lowered IR).
- Finally we fully lower =lir= to =LLVM IR= and pass it to the object generator
  to generate object files.
- Call the default =c compiler= to link the object files and generate the machine code.
* DONE Episode 4 - The reader
CLOSED: [2021-07-27 Tue 22:50]
** What is a Parser ?
To put it simply, Parser converts the source code to an [[https://en.wikipedia.org/wiki/Abstract_syntax_tree][AST]]
*** Algorithms
- LL(k)
- LR
- LALR
- PEG
- .....

Read More:
- https://stereobooster.com/posts/an-overview-of-parsing-algorithms/
- https://tomassetti.me/guide-parsing-algorithms-terminology/
*** Libraries
- https://en.wikipedia.org/wiki/Comparison_of_parser_generators
*** Our Parser
- We have a hand written LL(1.5) like parser/lexer since lisp already has a structure.
#+BEGIN_SRC lisp
  ;; pseudo code
  (def some-fn (fn (x y)
                   (+ x y)))
  (defn main ()
    (println "Result: " (some-fn 3 8)))
#+END_SRC
- LL(1.5)?
- O(n)
* DONE Episode 5 - The Abstract Syntax Tree
CLOSED: [2021-07-30 Fri 14:01]
** What is an AST?
Ast is a tree representation of the abstract syntactic structure of source code. It's just a tree made of nodes that each node is
a data structure describing the syntax.

#+BEGIN_SRC lisp
  ;; pseudo code
  (def main (fn () 4))
  (prn (main))
#+END_SRC


[[./imgs/ast.svg]]
** The =Expression= abstract class
*** Expressions
- Expressions vs Statements
- Serene(Lisp) and expressions
** Node & AST
* DONE Episode 6 - The Semantic Analyzer
CLOSED: [2021-08-21 Sat 18:44]
** Qs
- Why didn't we implement a linked list?
- Why we are using the =std::vector= instead of llvm collections?
** What is Semantic Analysis?
- Semantic Analysis makes sure that the given program is semantically correct.
- Type checkr works as part of this step as well.

#+BEGIN_SRC lisp
  ;; pseudo code
  (4 main)
#+END_SRC

[[./imgs/incorrct_semantic.svg]]
** Semantic Analysis and rewrites
We need to reform the AST to reflect the semantics of Serene closly.

#+BEGIN_SRC lisp
  ;; pseudo code
  (def main (fn () 4))
  (prn (main))
#+END_SRC
[[./imgs/ast.svg]]

[[./imgs/semantic.svg]]

Let's run the compiler to see the semantic analysis in action.
** Let's check out the code
* DONE Episode 7 - The Context and Namespace
CLOSED: [2021-09-04 Sat 10:53]
** Namespaces
*** Unit of compilation
*** Usually maps to a file
*** keeps the state and evironment
** SereneContext vs LLVM Context vs MLIR Context
*** Compilers global state
*** The owner of LLVM/MLIR contexts
*** Holds the namespace table
*** Probably will contain the primitive types as well
* DONE Episode 8 - MLIR Basics
CLOSED: [2021-09-17 Fri 10:18]
** Serene Changes
- Introducing a SourceManager
- Reader changes
- *serenec* cli interface in changing

** Disclaimer
*I'm not an expert in MLIR*

** Why?
- A bit of history
- LLVM IR is to low level
- We need an IR to implement high level concepts and flows
 *MLIR* is a framework to build a compiler with your own IR. kinda :P

- Reusability
- ...
** Language
*** Overview
- SSA Based (https://en.wikipedia.org/wiki/Static_single_assignment_form)
- Typed
- Context free(for lack of better words)

*** Dialects
- A collection of operations
- Custom types
- Meta data
- We can use a mixture of different dialects
**** builtin dialects:
- std
- llvm
- math
- async
- ...

*** Opetations
- Higher level of abstraction
- Not instructions
- SSA forms
- Tablegen backend
- Verifiers and printers
*** Attributes
*** Blocks & Regions
*** Types
- Extesible
** Pass Infrastructure
Analysis and transformation infrastructure

- We will implement most of our semantic analysis logic and type checker as passes

** Pattern Rewriting
- Tablegen backed
** Operation Definition Specification
** Examples
*Not*: You need =mlir-mode= and =llvm-mode= available to you for the code highlighting of
the following code blocks. Both of those are distributed with the LLVM.

*** General syntax
#+BEGIN_SRC mlir
   %result:2 = "somedialect.blah"(%x#2) { some.attribute = true, other_attribute = 3 }
               : (!somedialect<"example_type">) -> (!somedialect<"foo_s">, i8)
                  loc(callsite("main" at "main.srn":10:8))
#+END_SRC

*** Blocks and Regions
#+BEGIN_SRC mlir
  func @simple(i64, i1) -> i64 {
  ^bb0(%a: i64, %cond: i1): // Code dominated by ^bb0 may refer to %a
    cond_br %cond, ^bb1, ^bb2

  ^bb1:
    br ^bb3(%a: i64)    // Branch passes %a as the argument

  ^bb2:
    %b = addi %a, %a : i64
    br ^bb3(%b: i64)    // Branch passes %b as the argument

  // ^bb3 receives an argument, named %c, from predecessors
  // and passes it on to bb4 along with %a. %a is referenced
  // directly from its defining operation and is not passed through
  // an argument of ^bb3.
  ^bb3(%c: i64):
    //br ^bb4(%c, %a : i64, i64)
    "serene.ifop"(%c) ({ // if %a is in-scope in the containing region...
         // then %a is in-scope here too.
          %new_value = "another_op"(%c) : (i64) -> (i64)

          ^someblock(%new_value):
            %x = "some_other_op"() {value = 4 : i64} : () -> i64

    }) : (i64) -> (i64)
  ^bb4(%d : i64, %e : i64):
    %0 = addi %d, %e : i64
    return %0 : i64   // Return is also a terminator.
  }
#+END_SRC

*** SLIR example
Command line arguments to emir =slir=
#+BEGIN_SRC sh
  ./builder run --build-dir ./build -emit slir `pwd`/docs/examples/hello_world.srn
#+END_SRC

Output:
#+BEGIN_SRC mlir
  module @user  {
    %0 = "serene.fn"() ( {
      %2 = "serene.value"() {value = 0 : i64} : () -> i64
      return %2 : i64
    }) {args = {}, name = "main", sym_visibility = "public"} : () -> i64

    %1 = "serene.fn"() ( {
      %2 = "serene.value"() {value = 0 : i64} : () -> i64
      return %2 : i64
    }) {args = {n = i64, v = i64, y = i64}, name = "main1", sym_visibility = "public"} : () -> i64
  }
#+END_SRC

*** Serene's MLIR (maybe we need a better name)

Command line arguments to emir =mlir=
#+BEGIN_SRC sh
  ./builder run --build-dir ./build -emit mlir `pwd`/docs/examples/hello_world.srn
#+END_SRC

Output:
#+BEGIN_SRC mlir
module @user  {
  func @main() -> i64 {
    %c3_i64 = constant 3 : i64
    return %c3_i64 : i64
  }
  func @main1(%arg0: i64, %arg1: i64, %arg2: i64) -> i64 {
    %c3_i64 = constant 3 : i64
    return %c3_i64 : i64
  }
}
#+END_SRC

*** LIR
Command line arguments to emir =lir=
#+BEGIN_SRC sh
  ./builder run --build-dir ./build -emit lir `pwd`/docs/examples/hello_world.srn
#+END_SRC

Output:
#+BEGIN_SRC mlir
module @user  {
  llvm.func @main() -> i64 {
    %0 = llvm.mlir.constant(3 : i64) : i64
    llvm.return %0 : i64
  }
  llvm.func @main1(%arg0: i64, %arg1: i64, %arg2: i64) -> i64 {
    %0 = llvm.mlir.constant(3 : i64) : i64
    llvm.return %0 : i64
  }
}
#+END_SRC

*** LLVMIR
Command line arguments to emir =llvmir=
#+BEGIN_SRC sh
  ./builder run --build-dir ./build -emit ir `pwd`/docs/examples/hello_world.srn
#+END_SRC

Output:
#+BEGIN_SRC llvm
target datalayout = "e-m:e-p270:32:32-p271:32:32-p272:64:64-i64:64-f80:128-n8:16:32:64-S128"
target triple = "x86_64-unknown-linux-gnu"

declare i8* @malloc(i64 %0)

declare void @free(i8* %0)

define i64 @main() !dbg !3 {
  ret i64 3, !dbg !7
}

define i64 @main1(i64 %0, i64 %1, i64 %2) !dbg !9 {
  ret i64 3, !dbg !10
}

!llvm.dbg.cu = !{!0}
!llvm.module.flags = !{!2}

!0 = distinct !DICompileUnit(language: DW_LANG_C, file: !1, producer: "mlir", isOptimized: true, runtimeVersion: 0, emissionKind: FullDebug)
!1 = !DIFile(filename: "LLVMDialectModule", directory: "/")
!2 = !{i32 2, !"Debug Info Version", i32 3}
!3 = distinct !DISubprogram(name: "main", linkageName: "main", scope: null, file: !4, type: !5, spFlags: DISPFlagDefinition | DISPFlagOptimized, unit: !0, retainedNodes: !6)
!4 = !DIFile(filename: "REPL", directory: "/home/lxsameer/src/serene/serene/build")
!5 = !DISubroutineType(types: !6)
!6 = !{}
!7 = !DILocation(line: 0, column: 10, scope: !8)
!8 = !DILexicalBlockFile(scope: !3, file: !4, discriminator: 0)
!9 = distinct !DISubprogram(name: "main1", linkageName: "main1", scope: null, file: !4, line: 1, type: !5, scopeLine: 1, spFlags: DISPFlagDefinition | DISPFlagOptimized, unit: !0, retainedNodes: !6)
!10 = !DILocation(line: 1, column: 11, scope: !11)
!11 = !DILexicalBlockFile(scope: !9, file: !4, discriminator: 0)
#+END_SRC

** Resources
- [[https://www.youtube.com/watch?v=Y4SvqTtOIDk][2020 LLVM Developers’ Meeting: M. Amini & R. Riddle “MLIR Tutorial”]]
- [[https://www.youtube.com/watch?v=qzljG6DKgic][2019 EuroLLVM Developers’ Meeting: T. Shpeisman & C. Lattner “MLIR: Multi-Level Intermediate Repr..”]]
- https://mlir.llvm.org/docs
- https://mlir.llvm.org/docs/LangRef
- https://en.wikipedia.org/wiki/Basic_block

* DONE Episode 9 - IR (SLIR) generation
CLOSED: [2021-10-01 Fri 18:56]
** Updates:
- Source manager
- Diagnostic Engine
- JIT

There will be an episode dedicated to eache of these
** How does IR generation works
- Pass around MLIR context
- Create Builder objects that creates operations in specific
  locations
- ModuleOp
- Namespace
** How to define a new dialect
- Pure C++
- Tablegen
** SLIR
*** The SLIR goal
- An IR that follows the AST
- Rename?
*** Steps
- [X] Define the new dialect
- [X] Setup the tablegen
- [X] Define the operations
- [X] Walk the AST and generate the operations

* DONE Episode 10 - Pass Infrastructure
CLOSED: [2021-10-15 Fri 14:17]
** The next Step
** Updates:
*** CMake changes
** What is a Pass
*** Passes are the unit of abstraction for optimization and transformation in LLVM/MLIR
*** Compilation is all about transforming the input data and produce an output

Source code -> IR X -> IR Y -> IR Z -> ... -> Target Code

*** Almost like a function composition
*** The big picture
*** Pass Managers (Pipelines) are made out of a collection of passes and can be nested
*** The most of the interesting parts of the compiler reside in Passes.
*** We will probably spend most of our time working with passes

** Pass Infrastructure
*** ODS or C++
*** Operation is the main abstract unit of transformation
*** OperationPass is the base class for all the passes.
*** We need to override =runOnOperation=
*** There's some rules you need to follow when defining your Pass
**** Must not maintain any global mutable state
**** Must not modify the state of another operation not nested within the current operation being operated on
**** ...

*** Passes are either OpSpecific or OpAgnostic
**** OpSpecific
#+BEGIN_SRC C++
  struct MyFunctionPass : public PassWrapper<MyFunctionPass,
                                             OperationPass<FuncOp>> {
    void runOnOperation() override {
      // Get the current FuncOp operation being operated on.
      FuncOp f = getOperation();

      // Walk the operations within the function.
      f.walk([](Operation *inst) {
        // ....
      });
    }
  };

  /// Register this pass so that it can be built via from a textual pass pipeline.
  /// (Pass registration is discussed more below)
  void registerMyPass() {
    PassRegistration<MyFunctionPass>();
  }
#+END_SRC
**** OpAgnostic
#+BEGIN_SRC C++
  struct MyOperationPass : public PassWrapper<MyOperationPass, OperationPass<>> {
    void runOnOperation() override {
      // Get the current operation being operated on.
      Operation *op = getOperation();
      // ...
    }
  };
#+END_SRC
*** How transformation works?
*** Analyses and Passes
*** Pass management and nested pass managers
#+BEGIN_SRC C++
  // Create a top-level `PassManager` class. If an operation type is not
  // explicitly specific, the default is the builtin `module` operation.
  PassManager pm(ctx);

  // Note: We could also create the above `PassManager` this way.
  PassManager pm(ctx, /*operationName=*/"builtin.module");

  // Add a pass on the top-level module operation.
  pm.addPass(std::make_unique<MyModulePass>());

  // Nest a pass manager that operates on `spirv.module` operations nested
  // directly under the top-level module.
  OpPassManager &nestedModulePM = pm.nest<spirv::ModuleOp>();
  nestedModulePM.addPass(std::make_unique<MySPIRVModulePass>());

  // Nest a pass manager that operates on functions within the nested SPIRV
  // module.
  OpPassManager &nestedFunctionPM = nestedModulePM.nest<FuncOp>();
  nestedFunctionPM.addPass(std::make_unique<MyFunctionPass>());

  // Run the pass manager on the top-level module.
  ModuleOp m = ...;
  if (failed(pm.run(m))) {
    // Handle the failure
   }
#+END_SRC

* Episode 11 - Lowering SLIR
** Overview
- What is a Pass?
- Pass Manager
** Dialect lowering
*** Why?
*** Transforming a dialect to another dialect or LLVM IR
*** The goal is to lower SLIR to LLVM IR directly or indirectly.
** Dialect Conversions
This framework allows for transforming a set of illegal operations to a set of legal ones.
*** Target Conversion
*** Rewrite Patterns
*** Type Converter
** Full vs Partial Conversion